Grepr and Cribl both offer data pipelines for observability, but they differ in complexity and approach. Cribl is a powerful, flexible platform requiring significant setup, ongoing management, and learning its custom query language. Grepr is the newer, simpler option, using AI to automate data filtering and reduce manual configuration by 90%. While Cribl offers more integrations, Grepr supports common sources, uses familiar query languages, and enables faster, lower-maintenance deployment. Cribl suits large enterprises with dedicated teams, while Grepr is ideal for organizations seeking a faster, more automated solution.

Grepr reduces observability costs by storing all data in low-cost storage and using machine learning to forward only unique or summarized insights to platforms like DataDog, Splunk, or New Relic. Engineers can query retained data, generate reports, power AI, or trigger dynamic backfill during incidents—automatically via webhooks or manually through the Grepr interface. To learn more or request a demo, visit grepr.ai.

Everything we do at Grepr is around making sure we reduce costs with minimal impact to existing workflows. Grepr can automatically parse existing alerts in Datadog (Splunk and New Relic coming up in the next few weeks) and avoid modifying logs that power them. This way, you can roll out Grepr to prod without worrying about having to rewrite all your alerts.